Monte Carlo Tree Search: What kind of moves can easily be found and what kinds make trouble?
Whether the move is found and how quick it is found depends on a few things. If I understand correctly, there is a sequence of many "bad" moves which lead to the "big win" move, and you are afraid that the MCTS algorithm will not get to the "big win" move because it will be selecting more promising moves further up the tree. Some things to think about (read also the Wikipedia MCTS article):


when doing playouts, you can do play your game only for a few further moves or down to the game end. Playing only a few moves further is obviously quicker, but in the extreme case you described it would not be the best choice. If you know about the existence of such scenarios, ensure to play the game to the end in the playouts.
when doing playouts, you can choose your moves/actions either randomly or based on some simple, greedy (quick) heuristics tailored to your problem. Are there maybe greedy heuristics designed to find or take into account such scenarios for your game/problem? If yes, implement them. It is then called a "heavy playout". Compare the results to playouts using random moves.
If you choose the actions using UCT (Upper Confidence Bound applied to Trees), then the first part of the expression is responsible for exploitation. Moves with high average win ratio are preferred. The second part though corresponds to exploration. If the exploration parameter is set high enough (test empirically for your problem), then moves with few simulations will be preferred. High exploration would be another way to find your golden move, in detriment of exploitation (read about the exploration/exploitation dilemma).


If you describe a realistic game or problem scenario, we may be able to help you come up with a suitable strategy.